Bibliographic record
Abstract
Although caring and an ethics of care have been part of the nursing and education literature for many years, it has seldom been the focus of research and models in the HRD literature which has tended to be dominated by masculine rationality and models that focus on performance. In this paper, I argue that caring represents an important positive attribute of organizations and that a model of caring provides an alternative to HRD models based on masculine rationality and a performance philosophy. Research on caring in nursing and education is reviewed along with calls for an ethic of care in HRD. This is followed by a review of research on caring in organizations which provides the basis for the development of a model of caring in organizations for HRD. The model demonstrates the relationships between caring from three sources or levels in an organization (the organization or business unit, management, and co-workers), a climate of care for employees, and positive employee outcomes. HRD care-enhancing interventions for developing caring in organizations are then discussed. The paper concludes with a consideration of the implications of a model of caring for HRD research and practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".